This paper analyses the Granger-causality relationship between the growth of the real
GDP per capita and the public debt, here represented by the ratio of the current primary surplus/GDP and the ratio of the gross Government debt/GDP.
Using OECD annual data for 20 countries between 1988 and 2001, we adapt the methodology recently applied by Erdil and Yetkiner (2008) and we conclude that there is clear Granger causality and that it is always bi-directional. In addition, our findings point to a heterogeneous behaviour across the different countries.
These results have important policy implications since not only does public debt restrain economic growth, but also real GDP per capita growth influences the evolution of public debt.

We investigate the existence of Granger-causality between current account and government budget balances over the period 1970-2007, for different EU and OECD country groupings. We use a panel-data approach based on SUR systems and Wald tests with country specific bootstrap critical values. Our results show a causal relation from budget deficits to current account deficits for several EU countries: Bulgaria, Czech Republic, Estonia, Finland, France, Italy, Hungary, Lithuania, Poland, and Slovakia, along the lines of the so-called twin-deficit relationship. Considering the effective real exchange rate in the SUR system does not substantially alter the results.

The relationships between bank market concentration and bank efficiency are of particular relevance in the European Union (EU), but they remain controversial. Using a panel Granger causality approach, this paper contributes to the literature, testing not only the causality running from bank market concentration to bank efficiency, but also the reverse causality running from efficiency to concentration. The results obtained confirm the relative complexity of these causality relationships, although they generally point to a negative causation running both from concentration to efficiency and from efficiency to concentration. These findings are in line with the Structure Conduct Performance (SCP) paradigm and the suggestions that the increase of the banks’ market power will contribute to inefficiency, since these banks will face less competition to obtain more output results with less input costs. Our results suggest that within this panel of
all 27 EU countries over a relatively long time period, from 1996 to the onset of the 2008 financial crisis, the more cost-efficient commercial and savings banks operated in less concentrated markets.

This paper provides empirical evidence on the causality relations between bank performance and economic growth in a panel including 27 European Union member-states from 1996 through to the onset of the 2008 financial crisis. Bank performance is represented not only by the Return on Assets
(ROA) and Return on Equity (ROE) ratios but also by bank cost efficiency, measured through Data Envelopment Analysis (DEA). For economic growth, we consider not only the GDP per capita but also the gross fixed capital formation growth. Deploying a panel Granger causality approach, we confirm positive causality running from bank performance to economic growth. However, as regards the opposite causality, running from growth to bank performance, we conclude that economic growth positively contributes to the bank ROA and ROE ratios but not so certainly in the case of the DEA bank cost efficiency.

We investigated Granger-causality in the frequency domain between primary energy consumption/electricity consumption and GDP for the US by employing approach of Lemmens et al. (2008) and covering the period of January, 1973 to December, 2008. We found that causal and reverse causal relations between primary energy consumption and GDP and electricity consumption and GDP vary across frequencies. Our unique contribution in the existing literature lies in decomposing the causality on the basis of time horizons and demonstrating bidirectional the short-run, the medium-run and the long-run causality between GDP and primary energy consumption/electricity consumption and thus providing evidence for the feedback hypothesis. These results have important implications for the US for planning of the short, the medium and the long run energy and economic growth related policies.

Though there is a very large literature examining whether energy use Granger causes economic output or vice versa, it is fairly inconclusive. Almost all existing studies use relatively short time series, or panels with a relatively small time dimension. We apply Granger causality and cointegration techniques to a Swedish time series dataset spanning 150 years to test whether increases in energy use and energy quality have driven economic growth or vice versa. We show that these techniques are very sensitive to variable definition, choice of additional variables in the model, sample periods and size, and the introduction of structural breaks. The relationship between energy and growth may also have changed over time – energy causes output in the full sample while output causes energy use in recent smaller samples. Energy prices have a more robust causal impact on both energy use and output.; David Stern acknowledges funding from the Australian Research Council under Discovery Project DP120101088.

Though there is a very large literature examining whether energy use Granger causes economic output or vice versa, it is fairly inconclusive. Almost all existing studies use relatively short time series, or panels with a relatively small time dimension. We apply Granger causality and cointegration techniques to a Swedish time series dataset spanning 150 years to test whether increases in energy use and energy quality have driven
economic growth or vice versa. We show that these techniques are very sensitive to variable definition, choice of additional variables in the model, sample periods and size, and the introduction of structural breaks. The relationship between energy and growth may also have changed over time – energy causes output in the full sample while output causes energy use in recent smaller samples. Energy prices have a more robust causal impact on both energy use and output.; David Stern acknowledges funding from the Australian Research Council under Discovery Project DP120101088 and Kerstin Enflo acknowledges funding from the Wallander Research Foundation, grant number W2008:0357-1.

The paper analyses the causality between the Japanese-US relative export prices and the yen-dollar exchange rate. It explains why the Japanese yen proved strong even during the economic slump of the 1990s. The paper suggests that the appreciation of the Japanese yen forced the Japanese enterprises into price reductions and productivity increases, which put a floor under the high level of the yen and thus initiated rounds of appreciation. This corresponds to the conjecture of a vicious (virtuous) circle of appreciation and price adaptation.

Recent economic developments have shown the importance of spillover and contagion effects in financial markets as well as in macroeconomic reality. Such effects are not limited to relations between the levels of variables but also impact on the volatility and the distributions. We propose a method of testing restrictions for Granger noncausality on all these levels in the framework of Markov-switching Vector Autoregressive Models. The conditions for Granger noncausality for these models were derived by Warne (2000). Due to the nonlinearity of the restrictions, classical tests have limited use. We, therefore, choose a Bayesian approach to testing. The inference consists of a novel Gibbs sampling algorithm for estimation of the restricted models, and of standard methods of computing the Posterior Odds Ratio. The analysis may be applied to financial and macroeconomic time series with complicated properties, such as changes of parameter values over time and heteroskedasticity.

Recent economic developments have shown the importance of spillover and contagion effects in financial markets. Such effects are not limited to relations between the levels of financial variables but also impact on their volatility. I investigate Granger causality in conditional mean and conditional variances of time series. For this purpose a VARMA-GARCH model is used. I derive parametric restrictions for the hypothesis of noncausality in conditional variances between two groups of variables, when there are other variables in the system as well. These novel conditions are convenient for the analysis of potentially large systems of economic variables. Such systems should be considered in order to avoid the problem of omitted variable bias. Further, I propose a Bayesian Lindley-type testing procedure in order to evaluate hypotheses of noncausality. It avoids the singularity problem that may appear in the Wald test. Also, it relaxes the assumption of the existence of higher-order moments of the residuals required for the derivation of asymptotic results of the classical tests. In the empirical example, I find that the dollar-to-Euro exchange rate does not second-order cause the pound-to-Euro exchange rate, in the system of variables containing also the Swiss frank-to-Euro exchange rate...

This paper proposes a new nonparametric test for conditional independence, which is
based on the comparison of Bernstein copula densities using the Hellinger distance.
The test is easy to implement because it does not involve a weighting function in the
test statistic, and it can be applied in general settings since there is no restriction on the
dimension of the data. In fact, to apply the test, only a bandwidth is needed for the
nonparametric copula. We prove that the test statistic is asymptotically pivotal under
the null hypothesis, establish local power properties, and motivate the validity of the
bootstrap technique that we use in finite sample settings. A simulation study illustrates
the good size and power properties of the test. We illustrate the empirical relevance of
our test by focusing on Granger causality using financial time series data to test for
nonlinear leverage versus volatility feedback effects and to test for causality between
stock returns and trading volume. In a third application, we investigate Granger
causality between macroeconomic variables

We propose a nonparametric estimator and a nonparametric test for Granger causality measures that quantify linear and nonlinear Granger causality in distribution between random variables. We first show how to write the Granger causality measures in terms of copula densities. We suggest a consistent estimator for these causality measures based on nonparametric estimators of copula densities. Further, we prove that the nonparametric estimators are asymptotically normally distributed and we discuss the validity of a local smoothed bootstrap that we use in finite sample settings to compute a bootstrap bias-corrected estimator and test for our causality measures. A simulation study reveals that the bias-corrected bootstrap estimator of causality measures behaves well and the corresponding test has quite good finite sample size and power properties for a variety of typical data generating processes and different sample sizes. Finally, we illustrate the practical relevance of nonparametric causality measures by quantifying the Granger causality between S&P500 Index returns and many exchange rates (US/Canada, US/UK and US/Japen exchange rates).

Since the seminal work of Granger (1969), Granger causality has become a useful concept and tool in the study of the dynamic linkages between economic variables and to explore whether or not an economic variable helps forecast another one. Researchers have suggested a variety of methods to test the existence of Grangercausality in the literature. In particular, linear Granger causality testing has been remarkably developed; (see, for example, Toda & Philips (1993), Sims, Stock & Watson (1990), Geweke (1982), Hosoya (1991) and Hidalgo (2000)). However, in practice, the real economic relationship between different variables may often be nonlinear. Hiemstra & Jones (1994) and Nishiyama, Hitomi, Kawasaki & Jeong (2011) recently proposed different methods to test the existence of any non-linear Granger causality between a pair of economic variables under a α-mixing framework of data generating process. Their methods are general with nonparametric features, which however suffer from curse of dimensionality when high lag orders need to be taken into consideration in applications. In this thesis, the main objective is to develop a class of semiparametric time series regression models that are of partially linear structures, with statistical theory established under a more general framework of near epoch dependent (NED) data generating processes...

This article examines the cointegration level, changes in the existence and directions of causality of the foreign exchange (FX) rates in the Asian and emerging markets during the 1990s financial crises. Engle and Granger's simple bivariate and Johansen's multivariate cointegrations are applied to the FX rates for the 1994 Mexican, 1997 Asian, 1998 Russian, and 1999 Brazilian crises. In addition, the article conducts the Granger causality test and impulse response analysis to examine the causality pattern in all the FX rates. The analysis shows most of the pre-Mexican causality disappears and significant numbers of new causality emerge in the 1994 Mexican crisis while the 1997 Asian crisis generates significant spillover effects into the later part of the 1998 Russian and 1999 Brazilian crises.

This paper analyses if there is long term relationship and causality between accounting earnings and stock prices of Latin American firms. Cointegration tests are used in the same approach as Campbell e Shiller (1987) that investigated present value models based on rational expectations for the equity and bond markets. Essentially, if the variables are cointegrated, they have a long run relationship. This relation has been extensively studied for macroeconomic variables, but few works explore this issue for accounting and financial variables in emerging markets. Additionally the Granger causality between accounting earnings and stock prices are analyzed. Evidences points out that earnings and prices do have a long run relationship. However a causation relation can not be established for those variables. These findings can be explained by earnings timeliness (specially in Code Law countries) related by Ball, Kothari and Robin (2000), Collins et al. (1994) and Beaver, Lambert and Morse (1980). Additionally the evidences indicate that Argentine accounting earnings, that have less orthodox features than other Latin American countries, are typically stationary and have a higher degree of causality relation with stock prices than other Latin American countries accounting earnings.; En la línea de investigación de la relevancia de la información contable para mercados de capitales de países emergentes...